Predictive Accuracy of Machine Learning Models in Early Detection of Parkinson's Disease

Parkinson Disease (PD) is one of the neurodegenerative diseases that significantly impair both motor and mentally abilities. Early identification is central to efficient outcome of therapeutic intervention, inhibition of disease pathology. The aim of the present study is to examine the predictive accuracy of some machine-learning methods in predicting early-stage PD with the integrated clinical and sensor data. An in-depth analysis is carried out with the use of well-known and popular families of algorithms, among which it is worth highlighting Support Vector Machines (SVM), Random Forests, and Neural Networks. The performance is measured by accuracy, precision, recall, and F 1 -score. The findings show that certain models, especially those with additions feature-selection strategies, produced promising accuracy in their results to differentiate between PD patients and healthy controls. Experimentally, these results confirm that machine-learning methods can improve early diagnostic challenges of Parkinson Disease in a significant way but that there remains a challenge of persistent issues regarding imbalance in available datasets and translation to real-life settings that need to be addressed.